Analysis date: 2020-01-10
library(plyr)
library(gtools)
library(openxlsx)
library(pheatmap)
library(reshape2)
library(progress)
library(Matrix)
library(Hmisc)
library(lemon)
library(ggpubr)
library(effsize)
library(ggbeeswarm)
library(ggfortify)
library(ggpmisc)
library(ggrepel)
library(readxl)
library(DESeq2)
library(TOSTER)
library(tidyverse)
library(vsn)
library(fdrtool)
library(limma)
library(apeglm)
library(IHW)
library(Rtsne)
library(biomartr)
library(biomaRt)
library(MultiAssayExperiment)
library(PMA)
library(gplots)
library(RColorBrewer)
library(grid)
library(ConsensusClusterPlus)
library(survival)
library(survminer)
library(cowplot)
library(viridis)
source("/Volumes/sd17b003/Sophie/Analysis/Screen_analysis/Figure_layouts.R")
load("/Volumes/sd17b003/Sophie/Analysis/CLL_Proteomics/CLL_Proteomics_final/Proteomics_Git/Robjects/CLL_Proteomics_Setup.RData")
load("/Volumes/sd17b003/Sophie/Analysis/CLL_Proteomics/CLL_Proteomics_final/Proteomics_Git/Robjects/CLL_Proteomics_ConsensusClustering.RData")
load("/Volumes/sd17b003/Sophie/Analysis/CLL_Proteomics/R_objects/GOterm_BCR.RData")
load("/Volumes/sd17b003/Sophie/Analysis/CLL_Proteomics/R_objects/GOterm_Spliceosome.RData")
colData(multiomics_MAE)$PG <- as.factor(CCP_group5[rownames(colData(multiomics_MAE))])
colData(multiomics_MAE)$CCP6_RNA <- as.factor(CCP_group6_RNA[rownames(colData(multiomics_MAE))])
set.seed(10)
rtsne_out <- Rtsne( t(assay(multiomics_MAE[prot_few_nas , ,"proteomics"])), perplexity = 10 )
rtsne_out_df <- rtsne_out$Y %>% as.data.frame() %>% as_tibble()
rtsne_out_df$pat_ID <- colnames(assay(multiomics_MAE[prot_few_nas , ,"proteomics"]))
rtsne_out_df <- left_join(rtsne_out_df,
wideFormat(multiomics_MAE[, ,c("SNPs","chrom_abber", "health_record_bin") ], colDataCols = c("gender", "treatment_status", "doehner_groups", "PG")) %>% as_tibble(),
by=c("pat_ID"="primary") )
rtsne_out_df <- mutate_at(rtsne_out_df, colnames(rtsne_out_df %>% dplyr::select(SNPs_ATM:health_record_bin_treated)), as.logical)
rtsne_out_df <- rtsne_out_df %>% replace(is.na(.), "unknown")
message("t-SNE colored by Döhner groups")
ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes(color=doehner_groups), size=3) +
pp_sra +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
message("t-SNE colored by consensus cluster groups")
tsne_CCP_P_plot <- ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes(fill=PG), color="grey", shape=21) +
scale_fill_manual(values = colors_CCP)+
pp_sra +
guides(color=guide_legend(title="PG"))
tsne_CCP_P_plot+ theme(aspect.ratio=1, plot.title = element_text(size = 30))
message("t-SNE colored by combination of IGHV status and trisomy12")
tsne_IGHVtris_plot <- ggplot(rtsne_out_df %>% filter(health_record_bin_IGHV_mutated!="unknown", chrom_abber_trisomy12!="unknown"), aes(V1, V2)) +
geom_point(aes(fill=interaction(health_record_bin_IGHV_mutated, chrom_abber_trisomy12)), color="grey", shape=21) +
scale_fill_manual(values = colors_CCP[c(1,3,4,2)] )+
pp_sra_noguides
tsne_IGHVtris_plot + theme(aspect.ratio=1, plot.title = element_text(size = 30)) #+
message("t-SNE colored by IGHV status")
ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes_string(color="health_record_bin_IGHV_mutated"), size=3) +
scale_color_manual(values = c( "#0571b0", "#ca0020", "grey")) +
pp_sra +
theme(aspect.ratio=1, plot.title = element_text(size = 30))+
guides(color=guide_legend(title="IGHV_mutated"))
message("t-SNE colored by trisomy12")
ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes_string(color="chrom_abber_trisomy12"), size=3) +
scale_color_manual(values = c( "#0571b0", "#ca0020", "grey")) +
pp_sra +
theme(aspect.ratio=1, plot.title = element_text(size = 30))+
guides(color=guide_legend(title="trisomy12"))
sapply(colnames(rtsne_out_df)[c(4,5,8:29)], function(var){
print(ggplot(rtsne_out_df, aes(V1, V2)) +
geom_point(aes_string(color=var), size=3) +
scale_color_manual(values = c( "#0571b0", "#ca0020", "grey")) +
ggtitle(gsub("SNPs_", "", gsub("chrom_abber_", "", gsub( "health_record_bin_", "", var) ))) +
pp_sra_noguides +
theme(aspect.ratio=1, plot.title = element_text(size = 30)) )
})
## gender treatment_status SNPs_ATM SNPs_BIRC3 SNPs_EGR2 SNPs_NOTCH1
## data List,29 List,29 List,29 List,29 List,29 List,29
## layers List,1 List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2 List,2
## theme List,66 List,66 List,66 List,66 List,66 List,66
## coordinates ? ? ? ? ? ?
## facet ? ? ? ? ? ?
## plot_env ? ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4 List,4
## SNPs_POT1 SNPs_SF3B1 SNPs_TP53 SNPs_XPO1 chrom_abber_del11q
## data List,29 List,29 List,29 List,29 List,29
## layers List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2
## theme List,66 List,66 List,66 List,66 List,66
## coordinates ? ? ? ? ?
## facet ? ? ? ? ?
## plot_env ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4
## chrom_abber_del13q14 chrom_abber_del17p13 chrom_abber_del5_IgH
## data List,29 List,29 List,29
## layers List,1 List,1 List,1
## scales ? ? ?
## mapping List,2 List,2 List,2
## theme List,66 List,66 List,66
## coordinates ? ? ?
## facet ? ? ?
## plot_env ? ? ?
## labels List,4 List,4 List,4
## guides List,4 List,4 List,4
## chrom_abber_delIgH_break chrom_abber_gain14q32 chrom_abber_gain8q24
## data List,29 List,29 List,29
## layers List,1 List,1 List,1
## scales ? ? ?
## mapping List,2 List,2 List,2
## theme List,66 List,66 List,66
## coordinates ? ? ?
## facet ? ? ?
## plot_env ? ? ?
## labels List,4 List,4 List,4
## guides List,4 List,4 List,4
## chrom_abber_trisomy12 health_record_bin_elderly_at_diagnosis
## data List,29 List,29
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,66 List,66
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_elderly_patient health_record_bin_gender_binary
## data List,29 List,29
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,66 List,66
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_IGHV_mutated
## data List,29
## layers List,1
## scales ?
## mapping List,2
## theme List,66
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_komplex_abberant_karyotype
## data List,29
## layers List,1
## scales ?
## mapping List,2
## theme List,66
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_treated
## data List,29
## layers List,1
## scales ?
## mapping List,2
## theme List,66
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
message("There is are trisomy12 negative patients which clusters with all of the other trisomy12 patients. Does they have a subclonal mutations?")
metadata(multiomics_MAE)$fish_df_clonsizes["trisomy12" ,] %>%
dplyr::select(rtsne_out_df %>% filter(chrom_abber_trisomy12==FALSE) %>% arrange(desc(V1)) %>% slice(1:2) %>% .$pat_ID)
BCR_genes_mean <- assay(multiomics_MAE[BCR_genes$symbol, ,"proteomics"]) %>% colMeans(na.rm = TRUE) %>% enframe()
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
BCR_CCP_P_plot <- left_join(BCR_genes_mean,
enframe(multiomics_MAE$PG, value = "PG"),
by=c("name")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm() +
ggtitle("BCR protein abundance") +
scale_fill_manual(values=colors_CCP) +
pp_sra +
ylab("Mean abundance of BCR proteins")+
#theme(legend.position = "bottom") +
stat_compare_means(label = "p.signif", method = "t.test",
ref.group = ".all.", label.y = 0.2, hide.ns = TRUE) +
guides(fill=guide_legend(title="PG"))
BCR_CCP_P_plot + theme(aspect.ratio=1) +
stat_compare_means(method = "anova", label.y = 0.22, hjust=0)
# Smaller heatmap with only selected proteins
#sel_BCR <- c("ZAP70", "IGHM", "CD79A", "CD79B", "LYN", "SYK", "BLNK", "PLCG2", "BTK", "PTPN6", "NFATC2",
# "MAPK1", "MAP2K2", "NRAS", "MALT1", "BCL10", "PIK3CD","CD19", "VAV3",
# "AKT1", "IKBKB")
sel_BCR <- c("ZAP70", "IGHM", "CD79A", "CD79B", "SYK", "PLCG2", "BTK", "PTPN6",
"MAPK1", "PIK3CD", "AKT1", "IKBKB")
tmp_BCR <- wideFormat(multiomics_MAE[sel_BCR, ,"proteomics"], colDataCols = c("PG", "IGHV" )) %>%
as_tibble()
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
tmp_BCR_mx <- tmp_BCR %>% dplyr::select(-primary, -PG, -IGHV) %>% as.matrix()
rownames(tmp_BCR_mx) <- tmp_BCR$primary
colnames(tmp_BCR_mx) <- gsub("proteomics_", "", colnames(tmp_BCR_mx))
tmp_BCR_anno <- tmp_BCR[, c("PG", "IGHV")] %>% as.data.frame()
rownames(tmp_BCR_anno) <- tmp_BCR$primary
tmp_BCR_anno$IGHV[tmp_BCR_anno$IGHV=="U"] <- "U-CLL"
tmp_BCR_anno$IGHV[tmp_BCR_anno$IGHV=="M"] <- "M-CLL"
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ),
IGHV=c("U-CLL"= "#0571b0", "M-CLL"= "#ca0020"))
tmp_BCR_anno$PG <- factor(tmp_BCR_anno$PG, levels = c(1:3,5,6,4))
breaks= seq(min(tmp_BCR_mx), max(tmp_BCR_mx), 0.1)^2
breaks= sort(c(-breaks, breaks))
breaks <- breaks[! (breaks < min(tmp_BCR_mx) | breaks > max(tmp_BCR_mx) )]
pat_order_hclust <- sapply(c(1:3,5,6,4), function(P){
hc <- hclust(dist(tmp_BCR_mx[rownames(tmp_BCR_anno[tmp_BCR_anno$PG==P,]), ] ))
hc$labels[hc$order]
}) %>% unlist
PG_BCR_proteins_pheatmap <- pheatmap(t(tmp_BCR_mx[pat_order_hclust, ]),
annotation_col = tmp_BCR_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_BCR_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , cutree_rows = 4, show_colnames = F, treeheight_row = 0, fontsize_row = 5)
tmp_BCR_GO <- wideFormat(multiomics_MAE[unique(c(GO_BC_activation$hgnc_symbol, GO_BCR$hgnc_symbol)), ,"proteomics"], colDataCols = c("PG", "IGHV" )) %>%
as_tibble()
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
tmp_BCR_GO_mx <- tmp_BCR_GO %>% dplyr::select(-primary, -PG, -IGHV) %>% as.matrix()
rownames(tmp_BCR_GO_mx) <- tmp_BCR_GO$primary
colnames(tmp_BCR_GO_mx) <- gsub("proteomics_", "", colnames(tmp_BCR_GO_mx))
tmp_BCR_GO_anno <- tmp_BCR_GO[, c("PG", "IGHV")] %>% as.data.frame()
rownames(tmp_BCR_GO_anno) <- tmp_BCR_GO$primary
tmp_BCR_GO_anno$PG <- factor(tmp_BCR_GO_anno$PG, levels = c(1:3,5,6,4))
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ))
breaks= seq(min(tmp_BCR_GO_mx), max(tmp_BCR_GO_mx), 0.1)^2
breaks= sort(c(-breaks, breaks))
breaks <- breaks[! (breaks < min(tmp_BCR_GO_mx) | breaks > max(tmp_BCR_GO_mx) )]
message("Heatmap of abundance BCR signaling proteins ordered according to PG")
## Heatmap of abundance BCR signaling proteins ordered according to PG
PG_BCR_pheatmap <- pheatmap(t(tmp_BCR_GO_mx[(tmp_BCR_GO_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$rowname), ]),
annotation_col = tmp_BCR_GO_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_BCR_GO_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , cutree_rows = 3, show_colnames = F, treeheight_row = 0, fontsize_row = 5)
splice_genes_mean <- assay(multiomics_MAE[splice_genes$symbol, ,"proteomics"]) %>% colMeans(na.rm = TRUE) %>% enframe()
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
splicing_CCP_P_plot <- left_join(splice_genes_mean,
enframe(multiomics_MAE$PG, value = "PG"),
by=c("name")) %>%
filter(!is.na(PG)) %>%
ggplot(aes( PG, value ))+
geom_boxplot(aes(fill=PG)) + geom_beeswarm() +
ggtitle("Spliceosome protein abundance") +
pp_sra +
ylab("Mean abundance of spliceosome proteins")+
stat_compare_means(label = "p.signif", method = "t.test",
ref.group = ".all.", label.y = 0.2, hide.ns = TRUE)+
guides(fill=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
splicing_CCP_P_plot + theme(aspect.ratio=1) +
stat_compare_means(method = "anova", label.y = 0.22, hjust=0)
tmp_Splice_GO <- wideFormat(multiomics_MAE[GO_SpliceosomalComplex$hgnc_symbol, ,"proteomics"], colDataCols = c("PG", "IGHV" )) %>%
as_tibble()
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
tmp_Splice_GO_mx <- tmp_Splice_GO %>% dplyr::select(-primary, -PG, -IGHV) %>% as.matrix()
rownames(tmp_Splice_GO_mx) <- tmp_Splice_GO$primary
colnames(tmp_Splice_GO_mx) <- gsub("proteomics_", "", colnames(tmp_Splice_GO_mx))
tmp_Splice_GO_anno <- tmp_Splice_GO[, c("PG", "IGHV")] %>% as.data.frame()
rownames(tmp_Splice_GO_anno) <- tmp_Splice_GO$primary
tmp_Splice_GO_anno$PG <- factor(tmp_Splice_GO_anno$PG,levels = c(1:3,5,6,4))
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ))
breaks= seq(min(tmp_Splice_GO_mx), max(tmp_Splice_GO_mx), 0.1)^2
breaks= sort(c(-breaks, breaks))
breaks <- breaks[! (breaks < min(tmp_Splice_GO_mx) | breaks > max(tmp_Splice_GO_mx) )]
message("Heatmap of abundance spliceosome proteins ordered according to PG")
## Heatmap of abundance spliceosome proteins ordered according to PG
PG_GOSplice_pheatmap <- pheatmap(t(tmp_Splice_GO_mx[(tmp_Splice_GO_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$rowname), ]),
annotation_col = tmp_Splice_GO_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_Splice_GO_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , show_colnames = F , treeheight_row = 0, fontsize_row = 2)
some_spliceprots <- c("SF3B1", "SNRPA", "PRPF6", "PRPF3", "SF3A1", "SNRPD2", "SRSF4", "CDC5L", "PRPF19",
"CRNKL1", "PUF60", "PRPF8",
"SNRPB2")
tmp_Splice_GO <- wideFormat(multiomics_MAE[some_spliceprots, ,"proteomics"], colDataCols = c("PG", "IGHV" )) %>%
as_tibble()
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
tmp_Splice_GO_mx <- tmp_Splice_GO %>% dplyr::select(-primary, -PG, -IGHV) %>% as.matrix()
rownames(tmp_Splice_GO_mx) <- tmp_Splice_GO$primary
colnames(tmp_Splice_GO_mx) <- gsub("proteomics_", "", colnames(tmp_Splice_GO_mx))
tmp_Splice_GO_anno <- tmp_Splice_GO[, c("PG", "IGHV")] %>% as.data.frame()
rownames(tmp_Splice_GO_anno) <- tmp_Splice_GO$primary
tmp_Splice_GO_anno$PG <- factor(tmp_Splice_GO_anno$PG,levels = c(1:3,5,6,4))
SF3B1mut <- wideFormat(multiomics_MAE["SF3B1",,"SNPs"])$SNPs_SF3B1
## harmonizing input:
## removing 1 colData rownames not in sampleMap 'primary'
names(SF3B1mut) <- wideFormat(multiomics_MAE["SF3B1",,"SNPs"])$primary
## harmonizing input:
## removing 1 colData rownames not in sampleMap 'primary'
tmp_Splice_GO_anno$SF3B1 <- as.factor(SF3B1mut[rownames(tmp_Splice_GO_anno)])
tmp_Splice_GO_anno$IGHV[tmp_Splice_GO_anno$IGHV=="U"] <- "U-CLL"
tmp_Splice_GO_anno$IGHV[tmp_Splice_GO_anno$IGHV=="M"] <- "M-CLL"
ann_colors = list(
PG=c("1"= colors_CCP[1], "2"= colors_CCP[2], "3"= colors_CCP[3], "4"= colors_CCP[4], "5"= colors_CCP[5], "6"= colors_CCP[6] ),
SF3B1=c("1"="darkblue", "0"="gray80"),
IGHV=c("U-CLL"= "#0571b0", "M-CLL"= "#ca0020"))
pat_order_hclust_splice <- sapply(c(1:3,5,6,4), function(P){
hc <- hclust(dist(tmp_Splice_GO_mx[rownames(tmp_Splice_GO_anno[tmp_Splice_GO_anno$PG==P,]), ] ))
hc$labels[hc$order]
}) %>% unlist
PG_splice_proteins_pheatmap <- pheatmap(t(tmp_Splice_GO_mx[pat_order_hclust_splice, ]),
annotation_col = tmp_Splice_GO_anno, annotation_colors = ann_colors, scale = "row", cluster_cols = FALSE,
color = inferno(length(breaks)), border_color = NA,
gaps_col = (which(!tmp_Splice_GO_anno %>% rownames_to_column() %>% arrange(PG, IGHV) %>% .$PG %>% duplicated())-1)[-1],
breaks = breaks , show_colnames = F , treeheight_row = 0, fontsize_row = 5)
prot_pca <- prcomp(t( assay(multiomics_MAE[prot_few_nas , ,"proteomics"]) ))
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
prot_pca_x <- as_tibble(prot_pca$x[,1:10])
prot_pca_x$pat_ID <- colnames(assay(multiomics_MAE[prot_few_nas , ,"proteomics"]))
## harmonizing input:
## removing 13 colData rownames not in sampleMap 'primary'
prot_pca_x <- left_join(prot_pca_x,
wideFormat(multiomics_MAE[, ,c("SNPs","chrom_abber", "health_record_bin") ],
colDataCols = c("gender", "treatment_status", "PG")) %>% as_tibble() ,
by=c("pat_ID"="primary") )
## ExperimentList contains data.frame or DataFrame,
## potential for errors with mixed data types
prot_pca_x <- prot_pca_x %>% replace(is.na(.), "unknown")
ggplot(prot_pca_x, aes(PC1, PC2)) +
geom_point(aes(color=health_record_bin_IGHV_mutated, shape=chrom_abber_trisomy12)) + pp_sra +
scale_shape_manual(values = c( 16, 1, 4)) +
scale_color_manual(values = c( "#0571b0","#ca0020", "grey")) + theme(aspect.ratio = 1)
ggplot(prot_pca_x, aes(PC1, PC3)) +geom_point(aes(color=health_record_bin_IGHV_mutated, shape=chrom_abber_trisomy12)) +
scale_shape_manual(values = c( 16, 1, 4)) +
scale_color_manual(values = c( "#0571b0","#ca0020", "grey"))+ pp_sra+ theme(aspect.ratio = 1)
ggplot(prot_pca_x, aes(PC2, PC3)) +geom_point(aes(color=health_record_bin_IGHV_mutated, shape=chrom_abber_trisomy12)) +
scale_shape_manual(values = c( 16, 1, 4)) +
scale_color_manual(values = c( "#0571b0", "#ca0020", "grey")) + pp_sra+ theme(aspect.ratio = 1)
ggplot(prot_pca_x, aes(PC1, PC2)) +geom_point(aes(color=gender)) + pp_sra + scale_color_manual(values = c( "#0571b0","#ca0020", "grey")) + theme(aspect.ratio = 1)
PCA_CCP_1_2 <- ggplot(prot_pca_x, aes(PC1, PC2)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
PCA_CCP_1_2 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
PCA_CCP_1_3 <- ggplot(prot_pca_x, aes(PC1, PC3)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
PCA_CCP_1_3 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
PCA_CCP_2_3 <- ggplot(prot_pca_x, aes(PC2, PC3)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
PCA_CCP_2_3 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
PCA_CCP_1_4 <- ggplot(prot_pca_x, aes(PC1, PC4)) +
geom_point(aes(fill=PG),shape=21, color="grey") + pp_sra +
guides(color=guide_legend(title="PG")) +
scale_fill_manual(values = colors_CCP)
PCA_CCP_1_4 +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
genes_no_nas <- multiomics_MAE[["RNAseq_norm"]] %>% is.na() %>% rowSums()
genes_no_nas <- genes_no_nas[ genes_no_nas == 0 ] %>% names()
set.seed(10)
rtsne_out_RNA <- Rtsne( t(assay(multiomics_MAE[genes_no_nas , ,"RNAseq_norm"])), perplexity = 10 )
rtsne_out_RNA_df <- rtsne_out_RNA$Y %>% as.data.frame() %>% as_tibble()
rtsne_out_RNA_df$pat_ID <- colnames(assay(multiomics_MAE[genes_no_nas , ,"RNAseq_norm"]))
rtsne_out_RNA_df <- left_join(rtsne_out_RNA_df,
(wideFormat(multiomics_MAE[, ,c("SNPs","chrom_abber", "health_record_bin") ], colDataCols = c("gender", "treatment_status", "doehner_groups", "PG", "CCP6_RNA")) %>% as_tibble()),
by=c("pat_ID"="primary") )
rtsne_out_RNA_df <- mutate_at(rtsne_out_RNA_df, colnames(rtsne_out_RNA_df %>% dplyr::select(SNPs_ATM:health_record_bin_treated)), as.logical)
rtsne_out_RNA_df <- rtsne_out_RNA_df %>% replace(is.na(.), "unknown")
## Warning in `[<-.factor`(`*tmp*`, thisvar, value = "unknown"): invalid factor
## level, NA generated
ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes(color=doehner_groups), size=3) +
scale_color_manual(values = c("#377eb8","#e41a1c", "#984ea3", "#4daf4a", "#ff7f00", "grey"))+
pp_sra +
theme(aspect.ratio=1, plot.title = element_text(size = 30))
tsne_CCP_R_plot <- ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes(fill=PG), color="grey", shape=21) +
scale_fill_manual(values = colors_CCP)+
pp_sra +
guides(color=guide_legend(title="PG"))
tsne_CCP_R_plot + theme(aspect.ratio=1, plot.title = element_text(size = 30))
tsne_CCPRNA_R_plot <- ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes(fill=CCP6_RNA), color="grey", shape=21) +
scale_color_hue()+
pp_sra +
guides(color=guide_legend(title="CC_RNA"))
tsne_CCPRNA_R_plot+ theme(aspect.ratio=1, plot.title = element_text(size = 30))
sapply(colnames(rtsne_out_RNA_df)[c(4,5,9:30)], function(var){
print(ggplot(rtsne_out_RNA_df, aes(V1, V2)) +
geom_point(aes_string(color=var), size=3) +
scale_color_manual(values = c("#92c5de", "#f4a582", "grey")) +
ggtitle(gsub("SNPs_", "", gsub("chrom_abber_", "", gsub( "health_record_bin_", "", var) ))) +
pp_sra_noguides +
theme(aspect.ratio=1, plot.title = element_text(size = 30)) )
})
## gender treatment_status SNPs_ATM SNPs_BIRC3 SNPs_EGR2 SNPs_NOTCH1
## data List,30 List,30 List,30 List,30 List,30 List,30
## layers List,1 List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2 List,2
## theme List,66 List,66 List,66 List,66 List,66 List,66
## coordinates ? ? ? ? ? ?
## facet ? ? ? ? ? ?
## plot_env ? ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4 List,4
## SNPs_POT1 SNPs_SF3B1 SNPs_TP53 SNPs_XPO1 chrom_abber_del11q
## data List,30 List,30 List,30 List,30 List,30
## layers List,1 List,1 List,1 List,1 List,1
## scales ? ? ? ? ?
## mapping List,2 List,2 List,2 List,2 List,2
## theme List,66 List,66 List,66 List,66 List,66
## coordinates ? ? ? ? ?
## facet ? ? ? ? ?
## plot_env ? ? ? ? ?
## labels List,4 List,4 List,4 List,4 List,4
## guides List,4 List,4 List,4 List,4 List,4
## chrom_abber_del13q14 chrom_abber_del17p13 chrom_abber_del5_IgH
## data List,30 List,30 List,30
## layers List,1 List,1 List,1
## scales ? ? ?
## mapping List,2 List,2 List,2
## theme List,66 List,66 List,66
## coordinates ? ? ?
## facet ? ? ?
## plot_env ? ? ?
## labels List,4 List,4 List,4
## guides List,4 List,4 List,4
## chrom_abber_delIgH_break chrom_abber_gain14q32 chrom_abber_gain8q24
## data List,30 List,30 List,30
## layers List,1 List,1 List,1
## scales ? ? ?
## mapping List,2 List,2 List,2
## theme List,66 List,66 List,66
## coordinates ? ? ?
## facet ? ? ?
## plot_env ? ? ?
## labels List,4 List,4 List,4
## guides List,4 List,4 List,4
## chrom_abber_trisomy12 health_record_bin_elderly_at_diagnosis
## data List,30 List,30
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,66 List,66
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_elderly_patient health_record_bin_gender_binary
## data List,30 List,30
## layers List,1 List,1
## scales ? ?
## mapping List,2 List,2
## theme List,66 List,66
## coordinates ? ?
## facet ? ?
## plot_env ? ?
## labels List,4 List,4
## guides List,4 List,4
## health_record_bin_IGHV_mutated
## data List,30
## layers List,1
## scales ?
## mapping List,2
## theme List,66
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_komplex_abberant_karyotype
## data List,30
## layers List,1
## scales ?
## mapping List,2
## theme List,66
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
## health_record_bin_treated
## data List,30
## layers List,1
## scales ?
## mapping List,2
## theme List,66
## coordinates ?
## facet ?
## plot_env ?
## labels List,4
## guides List,4
sessionInfo()
## R version 3.5.2 (2018-12-20)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] viridis_0.5.1 viridisLite_0.3.0
## [3] cowplot_1.0.0 survminer_0.4.6
## [5] ConsensusClusterPlus_1.46.0 RColorBrewer_1.1-2
## [7] gplots_3.0.1.1 PMA_1.1
## [9] MultiAssayExperiment_1.8.3 biomaRt_2.38.0
## [11] biomartr_0.9.0 Rtsne_0.15
## [13] IHW_1.10.1 apeglm_1.4.2
## [15] limma_3.38.3 fdrtool_1.2.15
## [17] vsn_3.50.0 forcats_0.4.0
## [19] stringr_1.4.0 dplyr_0.8.3
## [21] purrr_0.3.3 readr_1.3.1
## [23] tidyr_1.0.0 tibble_2.1.3
## [25] tidyverse_1.3.0 TOSTER_0.3.4
## [27] DESeq2_1.22.2 SummarizedExperiment_1.12.0
## [29] DelayedArray_0.8.0 BiocParallel_1.16.6
## [31] matrixStats_0.55.0 Biobase_2.42.0
## [33] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2
## [35] IRanges_2.16.0 S4Vectors_0.20.1
## [37] BiocGenerics_0.28.0 readxl_1.3.1
## [39] ggrepel_0.8.1 ggpmisc_0.3.3
## [41] ggfortify_0.4.8 ggbeeswarm_0.6.0
## [43] effsize_0.7.6 ggpubr_0.2.4
## [45] magrittr_1.5 lemon_0.4.3
## [47] Hmisc_4.3-0 ggplot2_3.2.1
## [49] Formula_1.2-3 survival_3.1-8
## [51] lattice_0.20-38 Matrix_1.2-18
## [53] progress_1.2.2 reshape2_1.4.3
## [55] pheatmap_1.0.12 openxlsx_4.1.4
## [57] gtools_3.8.1 plyr_1.8.4
##
## loaded via a namespace (and not attached):
## [1] tidyselect_0.2.5 RSQLite_2.1.4 AnnotationDbi_1.44.0
## [4] htmlwidgets_1.5.1 munsell_0.5.0 codetools_0.2-16
## [7] preprocessCore_1.44.0 withr_2.1.2 colorspace_1.4-1
## [10] knitr_1.26 rstudioapi_0.10 ggsignif_0.6.0
## [13] labeling_0.3 slam_0.1-46 bbmle_1.0.20
## [16] GenomeInfoDbData_1.2.0 lpsymphony_1.10.0 KMsurv_0.1-5
## [19] farver_2.0.1 bit64_0.9-7 coda_0.19-3
## [22] vctrs_0.2.0 generics_0.0.2 xfun_0.11
## [25] R6_2.4.1 locfit_1.5-9.1 bitops_1.0-6
## [28] assertthat_0.2.1 scales_1.1.0 nnet_7.3-12
## [31] beeswarm_0.2.3 gtable_0.3.0 affy_1.60.0
## [34] rlang_0.4.2 zeallot_0.1.0 genefilter_1.64.0
## [37] splines_3.5.2 lazyeval_0.2.2 acepack_1.4.1
## [40] impute_1.56.0 broom_0.5.2 checkmate_1.9.4
## [43] BiocManager_1.30.10 yaml_2.2.0 modelr_0.1.5
## [46] backports_1.1.5 tools_3.5.2 affyio_1.52.0
## [49] ellipsis_0.3.0 Rcpp_1.0.3 base64enc_0.1-3
## [52] zlibbioc_1.28.0 RCurl_1.95-4.12 prettyunits_1.0.2
## [55] rpart_4.1-15 zoo_1.8-6 haven_2.2.0
## [58] cluster_2.1.0 fs_1.3.1 data.table_1.12.8
## [61] reprex_0.3.0 hms_0.5.2 evaluate_0.14
## [64] xtable_1.8-4 XML_3.98-1.20 emdbook_1.3.11
## [67] gridExtra_2.3 compiler_3.5.2 KernSmooth_2.23-16
## [70] crayon_1.3.4 htmltools_0.4.0 geneplotter_1.60.0
## [73] lubridate_1.7.4 DBI_1.0.0 dbplyr_1.4.2
## [76] MASS_7.3-51.4 cli_2.0.0 gdata_2.18.0
## [79] pkgconfig_2.0.3 km.ci_0.5-2 numDeriv_2016.8-1.1
## [82] foreign_0.8-72 xml2_1.2.2 annotate_1.60.1
## [85] vipor_0.4.5 XVector_0.22.0 rvest_0.3.5
## [88] digest_0.6.23 Biostrings_2.50.2 rmarkdown_1.18
## [91] cellranger_1.1.0 survMisc_0.5.5 htmlTable_1.13.3
## [94] curl_4.3 lifecycle_0.1.0 nlme_3.1-142
## [97] jsonlite_1.6 fansi_0.4.0 pillar_1.4.2
## [100] httr_1.4.1 glue_1.3.1 zip_2.0.4
## [103] bit_1.1-14 stringi_1.4.3 blob_1.2.0
## [106] latticeExtra_0.6-28 caTools_1.17.1.3 memoise_1.1.0